{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot Sequential Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A matplotlib utility function for visualizing results from [`feature_selection.SequentialFeatureSelector`](`../feature_selection/SequentialFeatureSelector.md`)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> from mlxtend.plotting import plot_sequential_feature_selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "for more information on sequential feature selection, please see [`feature_selection.SequentialFeatureSelector`](`../feature_selection/SequentialFeatureSelector.md`)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 1 - Plotting the results from SequentialFeatureSelector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cnGVKU5bmxgyNmTQNmZSfD1cX+pQyOrpp7+imszsfr0VGJp3u+R6nxvB764FkEAZMndQwpifJVS+TTvWp3soXjJ2dObbu6Or51ZZKiclNWaY2Z2lpDO0ujR5cXI0VSxlduRAwdnR0096Z66mxSJYymhvq77JdfzlybpSkU6K5IUNzovq4UDC6cwWefnEn+fhHnJJCg35TlklNMbhk0/7jwg1ZsV2vozuUMnZ0drN9V7KUAelUCBiTm8a2lDEUHkicS0ilRGMqBIqigoXg8szWDnKbd/ZUR7Y0ZpjcnGFKU0NPcPHuyK4oWcrYEaul2jtzWCG0kSoVShnZOi1lDMX4zr1zoyAl9QSKop4eY9s7eWbLLmI/BxqzaaY0eY+xiaT4XejsLrCrK8eOzm7ad3XTUa6U0ZgltQf+2PBA4tww9NtjLFdgy47OXj3GGrNppsRG/aaGDA0Z7zE2XuULRmd3nq5cPnat7epVykChh2A2k2L6OC9lDMXEOVLnRkE2E6oqknL5Att2dvP8tg4EFICGYo+x5izNDRmasqH3jTfq14dkKaOjK0d7opRRtKeXMoai1s9sXwx8E0gD3zezy0rWH0B4nO6+wGbgg2bWJumvga8nNn0lcJqZ3SjpauDNwNa47iwzW13L43CuGsUeYy2J7sj5QqFXjzER2memNDUwuTlLS2PGe4yNknzB6Mrl6eyOpYzYnmEF63Uf0kQrZQxFzT4VSWngCuBtQBuwUtJyM3s4sdlXgWvM7IeS3gJcCpxpZrcDh8d09gLWAbck9vusmd1Qq7w7V2vpVIrmhlSfHmMd3Tm27erq6fYZuiNnmNLcQEtjKLk0eI+xYUmWMjq7Y8BIlDLMQtD3UsbQ1TK8Hg2sM7P1AJKuA04CkoFkAXBunL4duLFMOu8Bfm1mO2uYV+fGXColmhoyNCWDS+wx9pctu8gXCmE7iebGDFOas0xuzHqPsTLKlTJ2dHSTz1tx/J89upSx/OdZ/u3SJp7edByzZsNll8IZZ9Tu/Wr5Cc4CNiTm24BjSrZ5ADiVUP31N8AUSXub2QuJbU4D/q1kv0skXQTcBnzOzDpHNOfO1YmBeow9v62Dv+R39vQMam7IMLU53OvS1JCmMTMxeoyFgBFKGe0doQG82GOqkChlTJogpYzlP89y4Web6dgVjrVtAyxZEtbVKpjUMpCUO2OlI4ycD1wu6Szgd8BGINeTgLQf8GpgRWKfC4C/AA3AMuCfgKV93lxaAiwBmDlzJq2trcM6iFzHLp5Ye++w9nW10dmxk/UPrRrrbNSd5DhwRZJIp8L4aintHtKnFtrb24f9d1ap4hh3uwcmtV5XleT4chPVvyx9XU8QKdq5E847r4NZs+6uyXvWMpC0AXMS87OBTckNzGwTcAqApMnAqWa2NbHJ+4BfmFl3Yp+n42SnpKsIwagPM1tGCDQsXLjQFi1aNKyDuPmW3zLv4KO8TrqOrH9oFQcdsnCsszEu5PIFunLhprhiySWbSTG1KcukRI+xkeiO3NraynD/zkr1KWV0dNHRlbwvI3S/zqZTE6KUMZBCAZ5Yn+KB+9M8cF+aZ58tP9Lvs882jdj5KVXLQLISmC/pQEJJ4zTgA8kNJO0DbDazAqGkcWVJGqfH5cl99jOzpxW6spwMPFSj/Ds37vXXY6y9Mw5gGRv10+nQY2xKS5aWeCPlaAxgmWzL2NWVY9uu2JaRGBW7GDCmtaS9Bxuw+QXxwH3pnsDx4AMZtm0Nn8ukSUZDI3SVqeyfO7d2eapZIDGznKRPEqql0sCVZrZW0lJglZktBxYBl0oyQtXWJ4r7S5pHKNHcUZL0tZL2JZReVwMfq9UxOLcnKtdjLF/sMfZCcXRkoRShx1jTyPQY68rl6crF+zI6crR3dLOrK9fzfIR0HDJkorRlVKKzAx5+aHfQeOD+DG1PhXavVMp4xSsLLH5XN4cdkeOwI/O8bH6BX93Uu40EoKUFLrmkdvmsaXcFM7sZuLlk2UWJ6RuAst14zewJQoN96fK3jGwunXPplEj322Os9wCWLcUeY3EAy9JHHpeWMrbvCvdl5Au7H5K2u5TR4KWMqLSKas39af7n4TS5XPh8XrpfgcOOzPOBD3Vy2JF5Dj40T0tL33ROPCW0BIReW4q9tjRue20558axgXqMPbetg6df7N1jrCuX56GnNtPRlQ+lmkQpo6Ux692TSxSrqNbcH0oca1b3rqJ69eF5/u5jnRx2RJ5Dj8gz86WVP6DqxFO6OfGUbv60ZiWve8MbmT6psVaHAXggcePI7r7xb2G//Y3PXNDR8+vLjY5yY4yZGbm8kS+E4DO1JeuljBLDqaJKj6Ph2DyQuHGhtG/8po3iws82A3gwGWOSyGZC9+LSccYmopGqohpPPJC4UZfLwbat4sXNYuuW8NqyRWx5MU6/uHs6rEuxcYMw6/0rt2OX+Px5zdy3Ms3ceQXmHVjggAMLzJlboKG2JXnnemx+QaFq6r6Rr6IaLzyQuGEbTkDY8qJo395/tUcqZUydZkyfYUybbuy1j3HQ/BxtT2XLbt/ZCb+8saHnD7eYxv6zjLnz8sw7qOBBxo2Ywaqo5o/zKqrh8kDiRiUg7L1vCAjTp4f56TMsTM9ITE8vMGUqpMrUjqy6O8OmjX3fb/9ZRuvK7by4WTz1RIonHk/xxPpUz7QHGTdcySqqYoP4ow+n6e7ec6uohssDyR5kPASE4frMBR19+sY3NYcGd4AZexkz9spz2JH5Pvt6kHGVqKSK6m//fs+uohouDyR1aE8OCMNV2jd+KL22ahFkDjiwwAHzPMiMV6VVVGtWp9nwZKiDmshVVMPlgaQf114Ln/88PPXUcey3f4HPXNA55N5Box0QisvqOSBUo9g3fiTH2hosyDz5eIonn/AgM54VCvDk46lEu0b5KqrTz+ya8FVUw+WBpIxrrw3DLu/cCSA2bUxz4fnNPPMXcdRr8h4QJohikDn8KA8y40mlVVSHHpHnMK+iGhEeSMr4/OeLQWS3jg7xr19u7rNtMiBMn9F/QJi+VyI4eEAY9zzI1IdkFVWxQdyrqEafB5IynnqqvzXGD36y0wOCG9BIBpkDDsyHAONBBrPeN/p5FVVf+YLR2Z2nK5fDCM9mGY2bRD2QlDF3Ljz5ZN/l+88y3rgo13eFcxXqL8iYwZYXPcgklVZRPfhAmq1bwkXRq6iCfKFAR1ee7nwBLASNaZMamNY8iZbGDHe3ZZjUWP4erJHkgaSMSy5JtpEEya6mzo00aWIHmc4OeGRtuldpo7SK6h3vzE34KqpcvhBLHIWe0sb0SY1Ma2mguTFD4yg8Q6YcDyRlFIdbDr22bNi9tpwbCXtakPEqqsp1x8CRyxcQkMmk2GtyI1NbGmiODyCrBx5I+nHGGeF18y238bJDFvqjdl1dqocgM9iozMkqqjWrw6u0iuqsJeHu8IlaRVWUDBwYNDak2TsGjpbGzIg8ErkWPJA4t4caapApTg8lyDxwX5qL/3fvUZn/93nN/P72DPk8XkU1iO5cgY7uPIVCgQLQnE2z95RGprU00tyQrtvAUcoDiXMT0EgFmZ7n5CZ0dYqbftbgVVRlhCdHFsgXCgA0ZdO8ZFoTU5obxlXgKOWBxDnXy1CCzGfP6XtvVUjD+N2920cju3Wt+MjhfD48MbK5IcPMYuBozJBN7xn3DtQ0kEhaDHwTSAPfN7PLStYfAFwJ7AtsBj5oZm1xXR54MG76lJmdGJcfCFwH7AXcB5xpZl21PA7nXFAaZL5+WVPZUZn323/itXMUH0Pc0ZWnYOH4JzVmeen0FiY3Z2lu2HMCR6maHZWkNHAFcDywADhd0oKSzb4KXGNmhwJLgUsT63aZ2eHxdWJi+VeAr5vZfOBF4OxaHYNzbmCfuaCDpubeQWOidJU3Czf/bdvZxZYdnWzd2YUk9t+rhVfOmsERB+7Dgjkz2H+vSUxtbthjgwjUtkRyNLDOzNYDSLoOOAl4OLHNAuDcOH07cONACSp0kH4L8IG46IfAxcB3RizXzrmKVTMq83hjZnTlQq+qQsFQKpQ4Zu09iUmNWVoa06Qn6DAXtQwks4ANifk24JiSbR4ATiVUf/0NMEXS3mb2AtAkaRWQAy4zsxuBvYEtZpZLpDmr3JtLWgIsAZg5cyatra3DOohcxy6eWHvvsPZ1tdHZsZP1D60a62y46JBXwJVXhfPS2BRa09c/NMaZGiFm9FRTAaRTIp0SqZRISbQDz4xd9gbV3t4+7GvfUNQykJS78aK04vR84HJJZwG/AzYSAgfAXDPbJOkg4L8kPQhsqyDNsNBsGbAMYOHChbZo0aIhHwDAzbf8lnkHH+X3kdSRkRxG3o2c8X5eCmZ0defp6A4dDISY0pJleksDk5uyNDVkSKfG13WgtbWV4V77hqKWgaQNmJOYnw1sSm5gZpuAUwAkTQZONbOtiXWY2XpJrcARwM+A6ZIysVTSJ03nnKtEIbZxdHbnwSCVElOas8yc3sKkxsy4DBxjpZaBZCUwP/ay2gicxu62DQAk7QNsNrMCcAGhBxeSZgA7zawzbvN64F/MzCTdDryH0HPrw8BNNTwG59weolAwOnN5urrzGJCSmNrcwH4zWmhpzNDckPGah2GqWSAxs5ykTwIrCN1/rzSztZKWAqvMbDmwCLhUkhGqtj4Rd38V8B+SCoSeZZeZWbGR/p+A6yR9Gbgf+EGtjsHVj3yhQL5g5PJG3oztu7pISbHOOkUq1l07V1QMHMkSx7RJDew/o4WWxixNDWkPHCOkpveRmNnNwM0lyy5KTN8A3FBmvzuBV/eT5npCjzC3h8gXrHegKIQB6szCfQtGGOW0OZumpTHLs+kU++81ia7ucJdw6LufC0NpR2J341kx2KRjA2mxsdTtWXqexRHbOFJpMb0lBI5JTVkasx44asXvbHc1VSgYuRgkQqAo9Nkmm07RmE3T0pyhMZOmMZsmm06RSafIpEUmnep1AXgyneKl0/uOtVGw8B75fIFc4v26cmHY7a7uAp25PB2dOQoFMKxXwAF6Ak4x2BSDj6s/ux/iFKqqsqkU01qyTNs7PIujKZsekyHVJyIPJG7YCmbk870DhcWukkIUzMikRVM2w+TGNE0NGRoyKbKZNJm0eoLFSF2oUxKpmG4lec/lCzHQhekQdMKrM5cPA+p15cgXLHZBFBbDjqRE1ZoHndEw2EOcGj1wjBkPJK6scr/uzXr/gk+lRGMm3fPrrymbDkEipViaSNVtu0VKqniAvHJVb8WgExpvQ6lnV2eegu3u9178nJQMOIlpv+gNLPkQp5REJq26eIiT68sDyQRkFn6FF+Iv8XzBKFiB5K0/KUFjNkNTNkNjQ4qmbChNFANENq0JcxdvuPAPLeiEgBOmu3MFuopVbN15OnMFdnTmQxtQopQDsVSV2h1sisFnIlwwc/kwpHqx+jObSTFjcgwcdfQQJ9eXB5I9jBVLEok2goIZJBquJXraIqY2Z2lsSNOYSYcgkShNuKErBp2GCv6ykqWcXKFAPh8G/SsGnK6eqrYQdIqKpcLxHnRKH+LUkN39ECcPHOOLB5JxpvTXbr7Q+8Z+CRpikJiSDf8Xg0Q2LdIxWIyXi82eLDTsM2jQMbPYppNoj8obXfnQtbU7tusUf81bmbEekr3Vkr3YRlN3rArM5QqYwrM49p7SyNTm+n76nxucB5I60rtapJBo5A1dYdHubrBhyIYUDT1BItVTovAgsWeRRFoinYJGBr7YFkukueIPjRh8ihfxYu+1ju5c76CTeD5V77ac4d+jU+4hTvtOHf8PcXJ9eSAZJclusMVAUVSsqih2g53aEhqvG7NpMqn+u8E6V0qxUTpcowcPOsWOFLs7VcTqtNiW05XL7+4pVXwPyt+jYwbbd3WFhzgBzY0ZXjKtianNDTR54NijeSAZAYVie0RPoEj+0YXG1EwqBIlkN9iGTJp0DbrBOlcJSWTTIpsGBmmPqOQeHcSEeIiT68sDSQWS9xvkY28nQa+qgMZsmW6wafWUKOq1G6xzlajkHp2Nj6XZf69Jo5grVy88kAwirVBkb2rI0JhN0ZzNkM2ENon0BOsG65xz5XggGURjNs0hc/ca62w451zd8p/SzjnnquKBxDnnXFU8kDjnnKsS1DDSAAAWtUlEQVSKBxLnnHNV8UDinHOuKh5InHPOVaWmgUTSYkmPSlon6XNl1h8g6TZJayS1Spodlx8u6S5Ja+O69yf2uVrS45JWx9fhtTwG55xzA6s4kEhqlvRXQ9g+DVwBHA8sAE6XtKBks68C15jZocBS4NK4fCfwITM7GFgMfEPS9MR+nzWzw+NrdaV5cs45N/IqCiSS3g2sBn4T5w+XtHyQ3Y4G1pnZejPrAq4DTirZZgFwW5y+vbjezB4zsz/F6U3As8C+leTVOefc6Kr0zvaLCYGhFcDMVkuaN8g+s4ANifk24JiSbR4ATgW+CfwNMEXS3mb2QnEDSUcDDcCfE/tdIukiQhD6nJl1lr65pCXAEoCZM2fS2to6SHbLa29vH/a+rjb8nNQnPy/1Z7TOSaWBJGdmW4f4nItyG5c+cud84HJJZwG/AzYCuZ4EpP2AHwEfNrPikLoXAH8hBJdlwD8RqsV6v5HZsriehQsX2qJFi4aS9x6tra0Md19XG35O6pOfl/ozWuek0kDykKQPAGlJ84FPAXcOsk8bMCcxPxvYlNwgVludAiBpMnCqmW2N81OBXwEXmtndiX2ejpOdkq4iBCPnnHNjpNLG9nOAg4FO4CfAVuAfBtlnJTBf0oGSGoDTgF7tKpL2kVTMwwXAlXF5A/ALQkP8f5bss1/8X8DJwEMVHoNzzrkaqKhEYmY7gc/HV0XMLCfpk8AKwqParjSztZKWAqvMbDmwCLhUkhGqtj4Rd38f8CZg71jtBXBW7KF1raR9CVVnq4GPVZon55xzI6+iQCLpVuC9ZrYlzs8ArjOzdwy0n5ndDNxcsuyixPQNwA1l9vsx8ON+0nxLJXl2zjk3Oiqt2tqnGEQAzOxF4CW1yZJzzrnxpNJAUpA0tzgj6QD69sByzjk3AVXaa+vzwB8k3RHn30S8R8M559zEVmlj+28kHQkcS2jkPtfMnq9pzpxzzo0LQ3lmeyOwOe6zQBJm9rvaZMs559x4UWmvra8A7wfWAsU7zItddp1zzk1glZZITgb+qtyYVs455ya2SnttrQeytcyIc8658anSEslOYLWk2wjDpABgZp+qSa6cc86NG5UGkuWUjJPlnHPOQeXdf39Y64w455wbnyrttTWf8BjcBUBTcbmZHVSjfDnnnBsnKm1svwr4DuGhU38NXEN44JRzzrkJrtJA0mxmtwEysyfN7GLAR+F1zjlXcWN7R3wA1Z/iM0Y24qP/Oueco/ISyT8ALYRH7B4FnAl8uFaZcs45N35U2mtrZZxsB/62dtlxzjk33lTaa2shYSj5A5L7mNmhNcqXc865caLSqq1rCT23TgXenXgNSNJiSY9KWifpc2XWHyDpNklrJLVKmp1Y92FJf4qvDyeWHyXpwZjmtySpwmNwzjlXA5UGkufMbLmZPR57bT1pZk8OtIOkNHAFcDzh/pPTJS0o2eyrwDWxZLOUcK8KkvYCvggcAxwNfDE+Jx5CN+QlwPz4WlzhMTjnnKuBSnttfVHS94HSsbZ+PsA+RwPrzGw9gKTrgJOAhxPbLADOjdO3AzfG6XcAt5rZ5rjvrcBiSa3AVDO7Ky6/hjAy8a8rPA7nnHMjrNJA8rfAKwkjACefRzJQIJkFbEjMtxFKGEkPEKrLvgn8DTBF0t797DsrvtrKLO9D0hLi44BnzpxJa2vrAFntX3t7+7D3dbXh56Q++XmpP6N1TioNJIeZ2auHmHa5tgsrmT8fuFzSWYSHZG0k3D3f376VpBkWmi0DlgEsXLjQFi1aVFGmS7W2tjLcfV1t+DmpT35e6s9onZNK20juLtO+MZg2YE5ifjawKbmBmW0ys1PM7AhCrzDMbOsA+7bF6X7TdM45N7oqDSRvIDyP5NHYw+pBSWsG2WclMF/SgZIagNMoGYpe0j7xjnmAC4Ar4/QK4O2SZsRG9rcDK8zsaWC7pGNjb60PATdVeAzOOedqoNKqrSH3jDKzXBxOZQWQBq40s7WSlgKrzGw5sAi4VFLx+e+fiPtulvTPhGAEsLTY8A58HLgaaCY0sntDu3POjaFBA0ksMfzKzA4ZauJmdjNwc8myixLTNwA39LPvlewuoSSXrwKGnBfnnHO1MWjVlpkVgAckzR2F/DjnnBtnKq3a2g9YK+keYEdxoZmdWJNcOeecGzcqDSRfqmkunHPOjVuVjv57h6SZwGvionvM7NnaZcs559x4UVH3X0nvA+4B3gu8D/ijpPfUMmPOOefGh0qrtj4PvKZYCpG0L/Bb+ulx5ZxzbuKo9IbEVElV1gtD2Nc559werNISyW8krQB+GuffT8n9Ic455yamAQOJpEYz6zSzz0o6hTBUioBlZvaLUcmhc865ujZYieQu4EhJPzKzMxl42HjnnHMT0GCBpCE+5vZ1sUTSyyAPtnLOOTcBDBZIPgacAUyn7zPaB3uwlXPOuQlgwEBiZn+QdCfQZmaXjFKenHPOjSOVDtr4rlHIi3POuXGo0ntBbpF0anyYlHPOOdej0vtIPgNMAvKSdhG6AJuZTa1Zzpxzzo0LlQ7aOKXWGXHOOTc+VTpooyR9UNIX4vwcSUfXNmvOOefGg0rbSL4NvBb4QJxvB64YbCdJiyU9KmmdpM+VWT9X0u2S7pe0RtIJcfkZklYnXgVJh8d1rTHN4rqXVHgMzjnnaqDSNpJjzOxISfcDmNmLkhoG2kFSmhBs3ga0ASslLTezhxObXQhcb2bfkbSAMH7XPDO7Frg2pvNq4CYzW53Y74z47HbnnHNjrNISSXcMDAY9w8gXBtnnaGCdma03sy7gOuCkkm0MKDbYTwM2lUnndHYPFumcc67OVFoi+RbwC+Alki4B3kMoTQxkFrAhMd8GHFOyzcWErsXnEHqFvbVMOu+nbwC6SlIe+BnwZTOz0p0kLQGWAMycOZPW1tZBsltee3v7sPd1teHnpD75eak/o3VOKu21da2ke4HjCF1/TzazRwbZrdw9J6UX/NOBq83sa5JeC/xI0iHxJkgkHQPsNLOHEvucYWYbJU0hBJIzgWvK5HkZsAxg4cKFtmjRokGPs5zW1laGu6+rDT8n9cnPS/0ZrXMy2DDyTYTxtl4OPAj8h5nlKky7DZiTmJ9N36qrs4HFAGZ2V3y/fYDiQ7ROo6Ray8w2xv+3S/oJoQqtTyBxzjk3OgZrI/khsJAQRI4HvjqEtFcC8yUdGBvmTwOWl2zzFKGUg6RXAU3Ac3E+RXhG/HXFjSVlJO0Tp7OEoVsewjnn3JgZrGprgZm9GkDSD4B7Kk3YzHKSPgmsANLAlWa2VtJSYJWZLQfOA74n6VxCtddZifaONxEGi1yfSLYRWBGDSJrw3PjvVZon55xzI2+wQNJdnIiBYUiJm9nNlDyS18wuSkw/DLy+n31bgWNLlu0AjhpSJpxzztXUYIHkMEnb4rSA5jjvY20555wDBn8eSXq0MuKcc258qvSGROecc64sDyTOOeeq4oHEOedcVTyQOOecq4oHEuecc1XxQOKcc64qHkicc85VxQOJc865qnggcc45VxUPJM4556rigcQ551xVPJA455yrigcS55xzVfFA4pxzrioeSJxzzlXFA4lzzrmq1DSQSFos6VFJ6yR9rsz6uZJul3S/pDWSTojL50naJWl1fH03sc9Rkh6MaX5LQ33+r3POuRFVs0AiKQ1cARwPLABOl7SgZLMLgevN7AjgNODbiXV/NrPD4+tjieXfAZYA8+Nrca2OwTnn3OBqWSI5GlhnZuvNrAu4DjipZBsDis99nwZsGihBSfsBU83sLjMz4Brg5JHNtnPOuaEY8JntVZoFbEjMtwHHlGxzMXCLpHOAScBbE+sOlHQ/sA240Mx+H9NsK0lzVrk3l7SEUHJh5syZtLa2Dusg2tvbh72vqw0/J/XJz0v9Ga1zUstAUq7twkrmTweuNrOvSXot8CNJhwBPA3PN7AVJRwE3Sjq4wjTDQrNlwDKAhQsX2qJFi4Z1EK2trQx3X1cbfk7qk5+X+jNa56SWgaQNmJOYn03fqquziW0cZnaXpCZgHzN7FuiMy++V9GfgFTHN2YOk6ZxzbhTVso1kJTBf0oGSGgiN6ctLtnkKOA5A0quAJuA5SfvGxnokHURoVF9vZk8D2yUdG3trfQi4qYbH4JxzbhA1K5GYWU7SJ4EVQBq40szWSloKrDKz5cB5wPcknUuoojrLzEzSm4ClknJAHviYmW2OSX8cuBpoBn4dX84558ZILau2MLObgZtLll2UmH4YeH2Z/X4G/KyfNFcBh4xsTp1zzg2X39nunHOuKh5InHPOVcUDiXPOuap4IHHOOVcVDyTOOeeq4oHEOedcVTyQOOecq4oHEuecc1XxQOKcc64qHkicc85VxQOJc865qnggcc45VxUPJM4556rigcQ551xVPJA455yrigcS55xzVfFA4pxzrioeSJxzzlWlpoFE0mJJj0paJ+lzZdbPlXS7pPslrZF0Qlz+Nkn3Snow/v+WxD6tMc3V8fWSWh6Dc865gdXsme2S0sAVwNuANmClpOXxOe1FFwLXm9l3JC0gPN99HvA88G4z2yTpEGAFMCux3xnx2e3OOefGWC1LJEcD68xsvZl1AdcBJ5VsY8DUOD0N2ARgZveb2aa4fC3QJKmxhnl1zjk3TLUMJLOADYn5NnqXKgAuBj4oqY1QGjmnTDqnAvebWWdi2VWxWusLkjSCeXbOOTdENavaAspd4K1k/nTgajP7mqTXAj+SdIiZFQAkHQx8BXh7Yp8zzGyjpCnAz4AzgWv6vLm0BFgCMHPmTFpbW4d1EO3t7cPe19WGn5P65Oel/ozWOallIGkD5iTmZxOrrhLOBhYDmNldkpqAfYBnJc0GfgF8yMz+XNzBzDbG/7dL+gmhCq1PIDGzZcAygIULF9qiRYuGdRCtra0Md19XG35O6pOfl/ozWuekllVbK4H5kg6U1ACcBiwv2eYp4DgASa8CmoDnJE0HfgVcYGb/XdxYUkbSPnE6C7wLeKiGx+Ccc24QNQskZpYDPknocfUIoXfWWklLJZ0YNzsP+KikB4CfAmeZmcX9Xg58oaSbbyOwQtIaYDWwEfherY7BOefc4GpZtYWZ3UxoRE8uuygx/TDw+jL7fRn4cj/JHjWSeXTOOVcdv7PdOedcVTyQOOecq4oHEuecc1XxQOKcc64qHkicc85VxQOJc865qnggcc45VxUPJM4556rigcQ551xVPJA455yrigcS55xzVfFA4pxzrioeSJxzzlXFA4lzzrmqeCBxzjlXFQ8kzjnnquKBxDnnXFU8kDjnnKuKBxLnnHNVqWkgkbRY0qOS1kn6XJn1cyXdLul+SWsknZBYd0Hc71FJ76g0Teecc6OrZoFEUhq4AjgeWACcLmlByWYXAteb2RHAacC3474L4vzBwGLg25LSFabpnHNuFNWyRHI0sM7M1ptZF3AdcFLJNgZMjdPTgE1x+iTgOjPrNLPHgXUxvUrSdM45N4oyNUx7FrAhMd8GHFOyzcXALZLOASYBb03se3fJvrPi9GBpAiBpCbAkzrZLenSI+S/aB3h+mPu62vBzUp/8vNSfas/JAZVsVMtAojLLrGT+dOBqM/uapNcCP5J0yAD7litBlaYZFpotA5YNIb9lSVplZgurTceNHD8n9cnPS/0ZrXNSy0DSBsxJzM9md9VV0dmENhDM7C5JTYQIOtC+g6XpnHNuFNWyjWQlMF/SgZIaCI3ny0u2eQo4DkDSq4Am4Lm43WmSGiUdCMwH7qkwTeecc6OoZiUSM8tJ+iSwAkgDV5rZWklLgVVmthw4D/iepHMJVVRnmZkBayVdDzwM5IBPmFkeoFyatTqGqOrqMTfi/JzUJz8v9WdUzonCdds555wbHr+z3TnnXFU8kDjnnKuKB5J+SLpS0rOSHhrrvLhA0pw4pM4jktZK+vRY58mBpCZJ90h6IJ6XL411nlwQRwS5X9Iva/k+Hkj6dzWxa7KrGzngPDN7FXAs8AkfIqcudAJvMbPDgMOBxZKOHeM8ueDTwCO1fhMPJP0ws98Bm8c6H243M3vazO6L09sJfyCzBt7L1ZoF7XE2G1/ei2eMSZoNvBP4fq3fywOJG5ckzQOOAP44tjlx0FOFshp4FrjVzPy8jL1vAP8IFGr9Rh5I3LgjaTLwM+AfzGzbWOfHgZnlzexwwmgTR8ehjtwYkfQu4Fkzu3c03s8DiRtXJGUJQeRaM/v5WOfH9WZmW4BWvH1xrL0eOFHSE4RR0t8i6ce1ejMPJG7ckCTgB8AjZvZvY50fF0jaV9L0ON1MGMX7f8Y2VxObmV1gZrPNbB5hKKn/MrMP1ur9PJD0Q9JPgbuAv5LUJunssc6T4/XAmYRfV6vj64TBdnI1tx9wu6Q1hPHwbjWzmnY3dfXFh0hxzjlXFS+ROOecq4oHEuecc1XxQOKcc64qHkicc85VxQOJc865qnggceOWJJP0tcT8+ZIuHqG0r5b0npFIa5D3eW8czfj2kuXzJO1KdHNeHR8vPdT050n6wMjl2Lm+PJC48awTOEXSPmOdkSRJ6SFsfjbwv8zsr8us+7OZHZ54dQ0jO/OAIQeSIR6Dm+A8kLjxLEd4JvW5pStKSxSS2uP/iyTdIel6SY9JukzSGfF5Gg9KelkimbdK+n3c7l1x/7Skf5W0UtIaSX+fSPd2ST8BHiyTn9Nj+g9J+kpcdhHwBuC7kv61kgOWNCk+K2dlfM7ESXH5vJjX++LrdXGXy4A3xhLNuZLOknR5Ir1fSlpU/IwkLZX0R+C1ko6Kn9W9klZI2i9u9ylJD8fjv66SfLs9nJn5y1/j8gW0A1OBJ4BpwPnAxXHd1cB7ktvG/xcBWwh3YzcCG4EvxXWfBr6R2P83hB9b84E2oAlYAlwYt2kEVgEHxnR3AAeWyef+wFPAvkAG+C/g5LiuFVhYZp95wC5gdXxdEZf/H+CDcXo68BgwCWgBmuLy+cCqxPH+MpHuWcDliflfAovitAHvi9NZ4E5g3zj/fuDKOL0JaCzmYay/B/4a+1dmkDjjXF0zs22SrgE+RbjwVmKlmT0NIOnPwC1x+YNAsorpejMrAH+StB54JfB24NBEaWca4cLdBdxjZo+Xeb/XAK1m9lx8z2uBNwE3DpLPP1sYUTfp7YTB+M6P803AXMLF/XJJhwN54BWDpF1OnjAgJsBfAYcAt4YhzkgDT8d1a4BrJd1YwTG4CcADidsTfAO4D7gqsSxHrLqNgz0mG6o7E9OFxHyB3n8TpeMHGSDgHDNbkVwRq4d29JM/DXoElRNwqpk9WvL+FwPPAIcRjrujn/17PpeoKTHdYWb5xPusNbPXlknjnYRAeCLwBUkHm1luqAfi9hzeRuLGPTPbDFxPaLguegI4Kk6fRKiqGar3SkrFdpODgEeBFcDH43D2SHqFpEmDpPNH4M2S9omN2KcDdwwjP8T3PycGRyQdEZdPA56OJagzCSUIgO3AlMT+TwCHx+OaAxzdz/s8Cuwr6bXxfbKSDpaUAuaY2e2EhyZNByYP81jcHsJLJG5P8TXgk4n57wE3SboHuI3+SwsDeZRwwZ8JfMzMOiR9n9B+cV+8mD8HnDxQImb2tKQLgNsJv/RvNrObhpEfgH8mlMDWxPd/AngX8G3gZ5LeG9+neLxrgJykBwjtPt8AHidU4z1EKMmVy3NXrL77lqRphGvFNwhtMj+OywR83cIzSNwE5qP/Ouecq4pXbTnnnKuKBxLnnHNV8UDinHOuKh5InHPOVcUDiXPOuap4IHHOOVcVDyTOOeeq8v8BC2XO3gqCyzsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mlxtend.plotting import plot_sequential_feature_selection as plot_sfs\n",
    "from mlxtend.feature_selection import SequentialFeatureSelector as SFS\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.datasets import load_iris\n",
    "\n",
    "iris = load_iris()\n",
    "X = iris.data\n",
    "y = iris.target\n",
    "knn = KNeighborsClassifier(n_neighbors=4)\n",
    "\n",
    "sfs = SFS(knn, \n",
    "          k_features=4, \n",
    "          forward=True, \n",
    "          floating=False,\n",
    "          scoring='accuracy',\n",
    "          cv=5)\n",
    "\n",
    "sfs = sfs.fit(X, y)\n",
    "\n",
    "fig1 = plot_sfs(sfs.get_metric_dict(),\n",
    "                kind='std_dev',\n",
    "                figsize=(6, 4))\n",
    "\n",
    "plt.ylim([0.8, 1])\n",
    "plt.title('Sequential Forward Selection (w. StdDev)')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "## plot_sequential_feature_selection\n",
      "\n",
      "*plot_sequential_feature_selection(metric_dict, figsize=None, kind='std_dev', color='blue', bcolor='steelblue', marker='o', alpha=0.2, ylabel='Performance', confidence_interval=0.95)*\n",
      "\n",
      "Plot feature selection results.\n",
      "\n",
      "**Parameters**\n",
      "\n",
      "- `metric_dict` : mlxtend.SequentialFeatureSelector.get_metric_dict() object\n",
      "\n",
      "\n",
      "- `figsize` : tuple (default: None)\n",
      "\n",
      "    Height and width of the figure\n",
      "\n",
      "- `kind` : str (default: \"std_dev\")\n",
      "\n",
      "    The kind of error bar or confidence interval in\n",
      "    {'std_dev', 'std_err', 'ci', None}.\n",
      "\n",
      "- `color` : str (default: \"blue\")\n",
      "\n",
      "    Color of the lineplot (accepts any matplotlib color name)\n",
      "\n",
      "- `bcolor` : str (default: \"steelblue\").\n",
      "\n",
      "    Color of the error bars / confidence intervals\n",
      "    (accepts any matplotlib color name).\n",
      "\n",
      "- `marker` : str (default: \"o\")\n",
      "\n",
      "    Marker of the line plot\n",
      "    (accepts any matplotlib marker name).\n",
      "\n",
      "- `alpha` : float in [0, 1] (default: 0.2)\n",
      "\n",
      "    Transparency of the error bars / confidence intervals.\n",
      "\n",
      "- `ylabel` : str (default: \"Performance\")\n",
      "\n",
      "    Y-axis label.\n",
      "\n",
      "- `confidence_interval` : float (default: 0.95)\n",
      "\n",
      "    Confidence level if `kind='ci'`.\n",
      "\n",
      "**Returns**\n",
      "\n",
      "- `fig` : matplotlib.pyplot.figure() object\n",
      "\n",
      "\n",
      "**Examples**\n",
      "\n",
      "For usage examples, please see\n",
      "    [http://rasbt.github.io/mlxtend/user_guide/plotting/plot_sequential_feature_selection/](http://rasbt.github.io/mlxtend/user_guide/plotting/plot_sequential_feature_selection/)\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with open('../../api_modules/mlxtend.plotting/plot_sequential_feature_selection.md', 'r') as f:\n",
    "    s = f.read()\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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